R2: Visualizing data NICAR 2023

Load packages

library(tidyverse)

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✔ ggplot2   3.4.1     ✔ tibble    3.1.8
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
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ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library(babynames)
library(scales)

Attaching package: ‘scales’

The following object is masked from ‘package:purrr’:

    discard

The following object is masked from ‘package:readr’:

    col_factor

Load data

data <- babynames

What is ggplot2?

The package ggplot2 allows us to create a number of visualizations with tremendous flexibility and customization. Because it is open source, its capabilities are expanded all the time by a community around the world.

ggplot examples

Making a bar chart

For this first chart, let’s make a bar chart.

We’ll start by getting our data ready.

bar_chart_data <- data %>% filter(year == 2017) %>% top_n(10, n)

Now we want to make a plot.

First, let’s create a blank canvas

data %>% ggplot()

Now let’s add x and y dimensions to our canvas.

bar_chart_data %>% ggplot(aes(x = name, y = n)) 

Now we need to add something called a ‘geom’. This determines what kind of chart will get added to our canvas.

bar_chart_data %>% ggplot(aes(x = name, y = n)) +
  geom_col()

This is not really how we want this to look. First, let’s order the bars by n.

bar_chart_data %>% ggplot(aes(x = reorder(name,desc(n)), y = n)) +
  geom_col()

That’s getting a bit better, but that color is hideous!

In R, we can use a selection of named colors, or any hex value.

bar_chart_data %>% ggplot(aes(x = reorder(name,desc(n)), y = n)) +
  geom_col(fill = "cadetblue", alpha = .8)

Looking better, but I don’t love the y-axis. Let’s add commas to the tick mark labels.

bar_chart_data %>% ggplot(aes(x = reorder(name,desc(n)), y = comma(n))) +
  geom_col(fill = "cadetblue", alpha = .8)

Now let’s get rid of that ugly background, those axis labels, and add a title to our chart.

bar_chart_data %>% ggplot(aes(x = reorder(name,desc(n)), y = comma(n))) +
  geom_col(fill = "cadetblue", alpha = .8) +
  labs(title = "The 10 most common baby names in 2017", subtitle = "None of these are Heaven spelled backwards.") +
  theme_minimal() +
  theme(axis.title = element_blank())

We can even layer geoms.

bar_chart_data %>% ggplot(aes(x = reorder(name,desc(n)), y = comma(n))) +
  geom_col(fill = "cadetblue", alpha = .8) +
  labs(title = "The 10 most common baby names in 2017", subtitle = "None of these are Heaven spelled backwards.", caption = "Source: Social Security Administration\nby Olga Pierce") +
  theme_minimal() +
  theme(axis.title = element_blank(),
        axis.text.y = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
geom_text(aes(label = comma(n)), vjust = -0.5)

Some of these are characterized as male names and some as female. What if we want to color code them?

bar_chart_data %>% ggplot(aes(x = reorder(name,desc(n)), y = comma(n), fill = sex)) +
  geom_col(alpha = .8) +
  labs(title = "The 10 most common baby names in 2017", subtitle = "None of these are Heaven spelled backwards.", caption = "Source: Social Security Administration\nby Olga Pierce") +
  theme_minimal() +
  theme(axis.title = element_blank(),
        axis.text.y = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
geom_text(aes(label = comma(n)), vjust = -0.5)

Those colors are kinda problematic, let’s pick our own.

bar_chart_data %>% ggplot(aes(x = reorder(name,desc(n)), y = comma(n), fill = sex)) +
  geom_col(alpha = .8) +
  labs(title = "The 10 most common baby names in 2017", subtitle = "None of these are Heaven spelled backwards.", caption = "Source: Social Security Administration\nby Olga Pierce") +
  theme_minimal() +
  theme(axis.title = element_blank(),
        axis.text.y = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
geom_text(aes(label = comma(n)), vjust = -0.5) +
scale_fill_manual(values=c("#756bb1", "#2ca25f"))

Now you try: With a buddy, make your own super awesome bar chart

Let’s make a line chart

First, we want to get the 10 girls’ names.

line_chart_data <- data %>% filter(sex == "F" & name %in% c("Olive", "Betty", "Madison", "Nevaeh", "Stephanie"))

View(line_chart_data)

Now we’ll set up our canvas

line_chart_data %>% ggplot(aes(x = year, y = prop))

And now we’ll add lines

line_chart_data %>% ggplot(aes(x = year, y = prop, color = name)) +
  geom_line() +
  theme_minimal()

Let’s do some cosmetic changes

line_chart_data %>% ggplot(aes(x = year, y = prop, color = name)) +
  geom_line() +
  scale_y_continuous(labels = scales::percent) +
  scale_x_continuous(breaks = seq(1900,2017,10)) +
  theme_minimal() +
  theme(axis.title = element_blank()) +
  labs(title = "My awesome chart title", subtitle = "Names as a percentage of all births", color = "Baby name")

And here’s an alternative version

line_chart_data %>% ggplot(aes(x = year, y = prop, color = name, fill = name)) +
  geom_density(stat = "identity", alpha = .5) +
  theme_minimal()

Faceting

(Ignore this code - it’s just to prep the data)

top_names <- data %>% group_by(name) %>% summarize(total = sum(n)) %>% arrange(desc(total)) %>% top_n(20,total)

facet_names <- inner_join(data, top_names)

Joining with `by = join_by(name)`

And now the chart

facet_names %>% ggplot(aes(x = year, y = prop, color = name)) +
  geom_line() +
  scale_y_continuous(labels = scales::percent) +
  scale_x_continuous(breaks = seq(1900,2017,40)) +
  theme_minimal() +
  theme(legend.position = "none") +
  facet_wrap(vars(name), ncol = 5)

Your turn: With a buddy, make a rad line chart